it could be not the answer for this case, but as I had the same error-message with .to_csvI tried .toCSV('name.csv') and the error-message was different ("SparseDataFrame' object has no attribute 'toCSV'). So the problem was solved by turning dataframe to dense dataframe df.to_de...
getsize(filename)start_time=time.time()_=pd.read_csv(filename)read_time=time.time()-start_timereturnwrite_time,read_time,file_size# 添加 Feather 格式支持deftest_feather(df,filename):start_time=time.time()df.to_feather(filename)write_time=time.time()-start_timefile_size=os.path.getsize...
You are reading compressed files and writing plaintext file. Could be IO bottleneck. Writing compressed file could speedup writing up to 10x stage.to_csv('output.csv.gz', sep='|', header=True, index=False, chunksize=100000, compression='gzip', encoding='utf-8') Additionally you could expe...
path1 = base +".compressed"path2 = base +".raw"withtm.ensure_clean(path1)asp1, tm.ensure_clean(path2)asp2: df = tm.makeDataFrame()# write to compressed filedf.to_pickle(p1, compression=compression)# decompresswithtm.decompress_file(p1, compression=compression)asf:withopen(p2,"wb")as...
使用pandas.read_csv(),您可以指定usecols来限制读入内存的列。并非所有可以被 pandas 读取的文件格式都提供读取子集列的选项。 使用高效的数据类型 默认的 pandas 数据类型并不是最节省内存的。特别是对于具有相对少量唯一值的文本数据列(通常称为“低基数”数据),这一点尤为明显。通过使用更高效的数据类型,您可以在...
df.to_csv("df_out.csv", index=False) ⇽--- index设为False表示不写入行索引 1. 以上代码写入的文件将如下所示: one,two,three 1,2,3 4,5,6 7,8,9 1. 2. 3. 4. 同理,可以将数据网格转换为JSON对象或直接写入文件: df.to_json() ⇽--- 如果给出文件路径做参数,就会把JSON数据写入该...
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另一个（更新的）解决方案是在polars中使用LazyFrame方法：
另一个（更新的）解决方案是在polars中使用LazyFrame方法：
Types['Function'][45:]['set_eng_float_format', 'show_versions', 'test', 'timedelta_range', 'to_datetime', 'to_numeric', 'to_pickle', 'to_timedelta', 'unique', 'value_counts', 'wide_to_long'] Function46 set_eng_float_format(accuracy: 'int' = 3, use_eng_prefix: 'bool' = ...